A neural network model of associative memory with higher order learning rule is presented. The new model could be fashioned to either the auto‐associative or the multiple associative mode. Energy function, asynchronous or synchronous dynamics can be constructed. The retrieval of the stored patterns or pattern sets from an incomplete input is monotonic guaranteed by a convergence theorem. The higher‐order correlation model show dramatic improvement in its storage capity in comparison to the conventional binary correlation model. It also opens up th posibility of storing spatial‐temporal patterns and symmetry invariant patterns.